This PDF file contains the front matter associated with SPIE Proceedings Volume 12123, including the Title Page, Copyright information, Table of Contents, and Committee Page.

Retinal imaging is the key to detecting several vision problems before it gets worse and may lead to severe vision loss or even blindness. Therefore, ophthalmologists suggest annual eye screenings for patients with pre-existing conditions such as diabetes. During eye exams, traditional fundus cameras are used to examine the retina. However, their large size, high price, and requirement of expertise hinder their usage at every health clinic. Therefore, smartphone-based imaging systems are an emerging research area to design small and affordable biomedical imaging devices. They may enable eye screening in remote clinics even by individuals at home. Smartphone-based retinal imaging systems are portable and have more compact designs compared to fundus cameras, so their captured images are likely to be low-quality with a smaller field of view. This paper investigates the smartphone-based portable retinal imaging systems available on the market including iExaminer, Peek Retina, D-Eye, and iNview. We first generate image distortion models for each smartphone-based system to visualize their lens distortion and light reflections on captured images. Then, we compare their image quality using deep learning based neural image assessment method to determine. Based on the results, the iNview system showed better image quality with a larger field of view compared with other smartphone-based devices.

Smart wearable devices have taken the market by storm. They are now their own device category on the consumer market, their popularity is unquestioned due to their ever-increasing set of functionalities and the vigorous competition between some of the biggest companies, and they seamlessly integrated into our everyday lives, as well as into professional contexts. New models appear on the market regularly, particularly since the sensory system of such devices is continuously developing, adding more ways of data acquisition and processing, along with projections and analyses. However, while the devices of certain subcategories are nearly identical with regard to their core functionalities, there may be significant differences in their specifications. Furthermore, the delivery of specifications towards the users is highly manufacturer-dependent and lacks coherent standardization. This is particularly relevant to professional contexts, such as defense, where individuals competently familiarizing themselves with their personal devices is essential. In this paper, we investigate the delivery of the specifications of the state-of-the-art smart wearable devices. We separately study the commonalities and best practices by device subcategories and usage contexts. We also highlight certain deviations on the current market and provide recommendations for the further evolution of such practices. The paper introduces the results of a study on documentation-related user behavior as well, in order to support future research.

Exosomes are cell-derived nanoscale (30-150 nm) vesicles found in body fluids. It has been reported that exosomes carry cell-of-origin specific nucleic acids, proteins and lipid molecules. Therefore, the potential of exosomes as biomarkers for disease detection has been intensively studied. Before any clinical usage of exosomes as biomarkers, in-depth characterization of exosomes, especially, analysis of inclusion molecules is essential. The majority of recent studies have focused on analyzing exosomal membrane proteins. In this study, we have utilized impedance spectroscopy (EIS) to analyze exosomes, which provides valuable information about the dielectric properties of exosomes. EIS is a label-free technique of characterizing samples suspended in a buffer solution. This study examined the magnitude and phase spectra of the exosomes produced by cultured non-cancerous (hTERT-HPNE) and cancerous (MIA PaCa-2) exosomes. EIS was measured using a microscale electrode device consisting of an FR4, a fiberglass laminate material for printed circuit boards, substrate with 10 ounces of copper, with 35 μm cladding. In addition, circuit models with constant phase element (CPE) for the exosome samples with electrodes were developed and analyzed to support our experimental findings. The results indicate that the impedance phase spectra can, in higher concentration samples, characterize samples using the magnitude and phase of the impedance.

Energy Landscape is a method to analyze fMRI resting state data to compare activity in regions of interest (ROIs) of the brain. The fMRI date from two groups, one with Attention Deficit Hyperactivity Disorder (ADHD) symptoms and another Control group, were examined using energy landscape analyzed. Preliminary results indicate potential biomarkers for ADHD in ROI network states that involve areas of the brain that control language, self-awareness, spatial attention, and visual processing.

Rapid, simple, inexpensive, and sensitive self-testing for SARS-CoV-2 is expected to be an important element of controlling the ongoing COVID pandemic. We report a novel approach in which saliva is mixed at room temperature with a Designer DNA Nanostructure (DDN) engineered to create a net-like structure that positions an array of highly specific nucleic acid aptamer-quencher locks at the locations of the trimeric spike proteins. When the spike proteins selectively unlock aptamers on the DDN, fluorescent reporter molecules are unquenched, generating an intense and easily measured optical signal. The fluorescence intensity, proportional to the virus concentration, is detected by a battery-powered palm-sized fluorimeter, whose functions are managed wirelessly with a Bluetooth-linked smartphone. Because the single-step, room temperature, test is performed in a conventional 0.2 mL PCR tube that is inserted into the fluorimeter, which resembles an Apple AirPods™ headphone case, we call the technology (DDN+fluorimeter+App) a “V-Pod.” We show that DDNs are highly specific only for detection of SARS-CoV-2 in both its initial form as well as common variants. The approach achieves a detection limit of 10,000 genome copies/mL, consistent with laboratory-based PCR, while requiring only one reagent and a 5-10 minute incubation time with saliva. Because DDNs are inexpensively synthesized, structurally stable nucleic acid constructs, and the V-Pod instrument is comprised of inexpensive electronic and photonic components, the approach offers potential for rapid self-monitoring of viral infection with integrated capability for contact tracing and interaction with health services.

Accumulating evidence suggests that cytokine storm syndrome (CSS) induced by the SARS-CoV-2 may be the ultimate cause of acute respiratory distress syndrome (ARDS), resulting in severe outcomes of COVID-19 infection and potentially death. Elevated levels of serum interleukin 6 (IL-6) correlate with the occurrence of respiratory failure, ARDS, and adverse clinical outcomes in many COVID-19 patients. The currently available clinical cytokine tests are costly, time-consuming, and require skilled technicians to execute. There is an unmet need for rapid, affordable, robust, and sensitive tests for cytokine levels. Therefore, this study aimed to develop a cost-effective system for quantitative detection of cytokines that can be used in the point-of-care (POC) format within a few minutes of blood collection. Our approach combines detection based on laser-induced breakdown spectroscopy with a lateral flow immunoassay (LIBS-LFIA) to deliver a quantitative clinical analysis platform with multiplexing capability. Lanthanide-complexed polymers (LCPs) were selected as the labels to provide optimal quantitative performance when sensing signals from the test lines of LFIAs. For a prototype implementation and a proof-of-concept, we targeted IL-6 as it is one of the most critical pro-inflammatory cytokines. Our initial LIBS-LFIA biosensor achieved a limit of detection (LOD) of 0.2298 μg/mL of IL-6 within 15 minutes and further sensitivity increase is possible with optimization. Regardless, since high levels of IL-6 are reported for patients in crisis, this is more than adequate to identify patients with highly elevated cytokine levels. Our research provides evidence that rapid and accurate detection of cytokines for clinical diagnosis and prognosis of COVID-19 and other pathogenic infections using LIBS is highly feasible and compatible with the POC format.

Heart failure (HF) is a common health condition that affects more than 600,000 Americans every year and results in their death. Luckily, machine learning classification, regression and prediction models are key approaches and techniques that can be used to detect and predict the cases of heart disease or failure. The study included in this paper based on a dataset that contains 918 instances or rows of various medical records. This research paper attempts to use these medical records to improve heart failure disease prediction accuracy. For that, multiple popular machine learning models were used to understand the data and provide a better prediction and results, based on different evaluation metrics. Furthermore, the results section in this study shows a better accuracy score compared with other related work using different machine learning algorithms and software. Finally, RStudio and Weka software are used in this paper to perform some of the algorithms and the best model results were using the random forest and logistic regression algorithms. These tools assisted us in better understanding of the data and data preprocessing.

It is estimated that by 2030, mental illness will cost global economy $16 trillion. To identify mental illness, we introduce electroencephalography (EEG) based connectivity biomarkers. EEG permits exploration of brain causal activities at high temporal resolution. Conventional EEG based brain connectivity studies are mostly describing connections among scalp electrode locations, which challenge the functional meaning interpretation of brain activities. In this work, we introduce a novel methodology to generate functional brain network biomarkers from source localized EEG for identifying human subjects that suffer from neurological disorders. We use sLORETA for source localization, post artifact removal, of EEG data followed by threshold binarization for marking activated and deactivated cortical estimates, and data-driven energy landscape analysis, which is rooted in statistical physics theory. This yields the brain subnetwork energy states. Furthermore, we demonstrate our novel method by a preliminary study where EEG data was recorded from 11 channels at 1000Hz from 22 schizophrenia patients and 27 healthy controls in response to transcranial magnetic stimulation administered on the left motor cortex. Sensorimotor network that is responsible for processing input and output of senses and motor activity comprises of precentral gyrus, postcentral gyrus, and paracentral gyrus was observed. In result, we found an energy state in the sensorimotor network, that significantly distinguished patients from controls (p-value<0.05) with Bonferroni correction. For future scope, we are observing other networks. Conclusively, we demonstrate a promising non-invasive low-cost data-driven method for brain network biomarker extraction at high spatiotemporal resolution for clinical applications.

The paper deals with the influence of birefringence on the change of polarization and the change of frequency in sensory optical fibers. The aim of the described research was to design and implement a set of measurements that analyze these influences. The article builds on research that has dealt with this issue in the past and offers some simplifications in the design of the measuring station, describes control measurements and analyzes in detail the response of light parameters at the sensor output to changes in temperature. The article presents, compares and discusses the results of intensity changes at individual wavelengths, which demonstrate changes not only in the instantaneous polarization state of light but also changes in wavelengths with maximum light intensity depending on temperature changes. These principles can then be used to advantage with Military, Biomedical and Physiological Sensors Systems.

Noninvasive electric stimulation-based treatments for neuropsychiatric disorders are of high interest in both research and clinical studies. Among them, transcranial magnetic stimulation (TMS) is widely accepted as a safe and effective method. Enhancing the performance of the apparatus requires stimulation of deeper brain regions which isn’t accessible with current coils due to the increased depth-spread tradeoff at deeper regions. In addition, focal rodent coils need to be developed to better understand brain stimulation mechanisms. Due to the smaller size of the rodent brain, a variety of challenges like the depth-spread tradeoff and high energy requirement arise when stimulating a functionally specific brain region. In this study, we have introduced tilted, wire-wrapped, multi-stacked coils for the purpose of enhancing brain stimulation for primates and non-primates. To improve the performance of the coils, we added different types of ferromagnetic cores to understand the efficacy of these cores on the distribution, decay rate, and the focality of the induced electric field. The analysis was performed using Finite Element Model (FEM) simulations, and the results were then verified using 3-d printed coils and experimental procedures. The performance of the coils was dependent on the relative permeability of the ferromagnetic core, demonstrating a general improvement in the focality and energy requirement of these TMS coils.

Diabetes mellitus, also known as just diabetes, is a medical condition marked by a high blood sugar level over long period of time. If diabetes left untreated it can result in damaging the nerves, kidney diseases, foot ulcers, damaging eyes, and in worst cases diabetes leads to death. The purpose of this study is to examine and compare numerous machine learning algorithms in order to determine the best forecasting algorithm based on various metrics such as accuracy, precision, recall, F-measure, kappa, sensitivity, and specificity. Four machine learning algorithms will be investigated in this paper such as Random Forest (RF), Support Victor Machine (SVM), K nearest neighbor (k-NN), and Classification and Regression Trees (CART). Algorithms are used in a comprehensive investigation on diabetes dataset. The obtained findings suggest that, when compared to other algorithms, RF provides more accurate predictions.

Breast cancer is the second most type of cancer diagnosed in women; it is also the leading cause of cancer caused deaths in women after lung cancer. Breast lumps can be classified as cancerous and non-cancerous. Non-cancerous breast lump development is very common in women. It is important to correctly diagnose the type of breast lump to administer the correct treatment and give the needed care and attention. Intensive research is being done to improve the diagnosis of the type of breast lumps. In this paper we will study different machine learning algorithms for the diagnosis of breast tumors and to predict whether its cancerous or non-cancerous. In this paper we will be building four different classification methods SVM, KNN, RF and CART. We will be using the breast cancer Wisconsin (diagnostic) dataset to train the models. We will base the performance of our models based on the accuracy and other classification evaluation parameters. For the final model we were able to achieve a prediction model with an f1 score of 0.9927.